&lt;title&gt;Experiments to support the development of techniques for hyperspectral mine detection&lt;/title&gt;

Winter, Edwin M.; Schlangen, Michael J.; Bowman, Anu P.; Carter, Michael R.; Bennett, C. L.; Fields, D. J.; Aimonetti, W.D.; Lucey, P. G.; Johnson, J. K.; Horton, Kyle G.; Williams, Tim; Stocker, Alan D.; Oshagan, Ara; DePersia, A. Trent; Sayre, Craig J.

doi:10.1117/12.241163

Cited by 5 publications

(3 citation statements)

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“…The prevailing hypothesis for the change in spectral contrast is a change in particle size of the soil [2,3]. Soils contain a wide range of particles of different sizes.…”

Section: Model Developmentmentioning

confidence: 99%

Comparison of a model of the disturbed soil spectrum to field observations

2010

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The spectral emissivity of soils in the region of thermal emission from 8 -14 micrometers is a combination of the spectral emission of the mineral and other components in the soil, as well as their physical arrangement and the thermal state of the soil (presence of thermal gradients). In this paper, we will outline the procedure for producing a spectral model of a mixed soil, and show examples of model soils compared to measured soils with the two major soil constituents: quartz and clay. The predictions of this theory are then compared to field measurements made with a LWIR Spectrometer of disturbed and undisturbed soil.

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“…The prevailing hypothesis for the change in spectral contrast is a change in particle size of the soil [2,3]. Soils contain a wide range of particles of different sizes.…”

Section: Model Developmentmentioning

confidence: 99%

Comparison of a model of the disturbed soil spectrum to field observations

2010

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“…Over the MWIR and LWIR regions, soil will show spectral structure due to specific spectral features of the minerals contained within. 7 Therefore, the simplest method of detection would be to observe a different spectral signature from the disturbed soil based solely on a change in mineral composition.…”

Section: Buried Landmine Signaturesmentioning

confidence: 99%

“…A spectral feature common to most soils is the silicate reststrahlen feature, which manifests itself in the 8.5 to 9.5 micron spectral window. 7 This feature can be exploited to detect buried objects. Soil particle size plays an important role in determining an observable emissivity difference in the reststrahlen bands between disturbed and undisturbed soil.…”

Section: Buried Landmine Signaturesmentioning

confidence: 99%

Surface and buried landmine scene generation and validation using the digital imaging and remote sensing image generation model

et al. 2004

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Detection and neutralization of surface-laid and buried landmines has been a slow and dangerous endeavor for military forces and humanitarian organizations throughout the world. In an effort to make the process faster and safer, scientists have begun to exploit the ever-evolving passive electro-optical realm, both from a broadband perspective and a multi or hyperspectral perspective. Carried with this exploitation is the development of mine detection algorithms that take advantage of spectral features exhibited by mine targets, only available in a multi or hyperspectral data set. Difficulty in algorithm development arises from a lack of robust data, which is needed to appropriately test the validity of an algorithm's results. This paper discusses the development of synthetic data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. A synthetic landmine scene has been modeled after data collected at a US Army arid testing site by the University of Hawaii's Airborne Hyperspectral Imager (AHI). The synthetic data has been created and validated to represent the surrogate minefield thermally, spatially, spectrally, and temporally over the 7.9 to 11.5 micron region using 70 bands of data. Validation of the scene has been accomplished by direct comparison to the AHI truth data using qualitative band to band visual analysis, Rank Order Correlation comparison, Principle Components dimensionality analysis, and an evaluation of the R(x) algorithm's performance. This paper discusses landmine detection phenomenology, describes the steps taken to build the scene, modeling methods utilized to overcome input parameter limitations, and compares the synthetic scene to truth data.

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